Cloud climatology of the Indian Ocean based on ship observations

Surface cloud data based on synoptic observations made by Voluntary Observing Ships (VOS) during the period 1951-98 were used to prepare the seasonal and annual cloud climatology of the Indian Ocean. The analysis has been carried out by separating the long-term trends, decadal and inter-annual components from the monthly cloud anomaly time series at each 5° × 5° grids. Maximum zone of total and low cloud cover shifts from equator to northern parts of India during the monsoon season. During the monsoon season (June-September), maximum total cloud cover exceeding 70% and low cloud cover exceeding 50% are observed over north Bay of Bengal. Maximum standard deviation of total and low cloud cover is observed near the equator and in the southern hemisphere. Both total and low cloud cover over Arabian Sea and the equatorial Indian Ocean are observed to decrease during the ENSO events. However, cloud cover over Bay of Bengal is not modulated by the ENSO events. On inter-decadal scale, low cloud cover shifted from a "low regime" to a "high regime" after 1980 which may be associated with the corresponding inter-decadal changes of sea surface temperatures over north Indian Ocean observed during the late 1970s.

Low level wind shear over Bomba airport

Significant climatological features based on 329 Low Level Wind Shear (LLWS) reports from 1985 to 1989 at Bombay airport are presented, The monsoon season has the highest frequency of occurrence of LLWS mainly due to thunderstorms and strong gusty winds, Other than monsoon season, occurrence of LLWS is related to sea and land breeze and nocturnal increase of surface temperature during night. The preferred time of occurrence of LLWS is between 0000 to 0600 IST and 1800 to 240J IST. The simultaneous occurrence of strong and severe LL WS, low cloud ceiling and very poor visibility has an adverse effect on aircraft operations at Bombay airport during landing and take-off.

Normalized anomaly patterns of meteorological elements: A case study of Northeast monsoon 2002 and 2004

ekSle foKkfud vk¡dM+ksa dks lkekU;h—r folaxfr;ksa ds laca/k esa crkuk izk;% lgk;d jgrk gS D;ksafd blls lkekU; cuke vlkekU; ekuksa dks igpkuuk ljy gks tkrk gSA blds vykok blls LFkku ds izHkko rFkk vk¡dM+ksa ds izlkj dk izHkko nwj gksrk gS vkSj nks fHkUu LFkkuksa esa izs{k.kksa dh rqyuk lqfo/kktud gks tkrh gSA bl izdkj lkekU;h—r folaxfr ¼,u- ,-½ iSVuZ vFkkZr fu/kkZfjr le; esa folaxfr;ksa dk LFkkfud forj.k izfrdwy ekSle dh ?kVukvksa esa iwokZuqekudrkZvksa ds fy, ,d l’kDr midj.k cu tkrk gSA bl 'kks/k i= esa mRrjiwohZ ekWulwu 2002 dh varj&ekSleh fof’k"V iz—fr ij fopkj djrs gq, ekSle dh izfrdwy ?kVukvksa dk fo’ys"k.k djus ds fy, ,u- ,- iSVuZ ds mi;ksx ij dk;Z fd;k x;k gSA mRrj iwohZ ekWulwu 2002 ds nkSjku lw[ks tSlh fLFkfr;ksa ds ckjs esa foLrkj ls ppkZ dh xbZ gS vkSj muds dkj.kksa dh tk¡p dh xbZ gSA ;g Hkh ns[kk x;k gS fd mRrj iwohZ ekWulwu 2002 ds varj ekSleh iz—fr iSVuZ esa izsf{kr lw[ks tSls fLFkfr dk ,d dkj.k 200 ,p- ih- ,- Åijh ry fjt dk gksuk vFkok ldkjkRed HkwfoHko Å¡pkbZ folaxfr] uoEcj esa lkbcsfj;u gkbZ esa udkjkRed ek/; leqnz Lrj nkc folaxfr] 200 ,p- ih- ,- iou folaxfr dh rhozrk gks ldrk gSA fuEu es?k ek=k] 'kq"d cYc rkieku vkSj lkis{k vknzZrk ls mRrj iwohZ ekWulwu 2002 esa lw[ks tSlh fLFkfr;ksa dk irk pyk tcfd vkSlr iou xfr ds ,u- ,- ls caxky dh [kkM+h esa pØokrksa ds {kh.k gksus vkSj izk;}hih; Hkkjr rd ugha igq¡pus ds ckjs esa irk pykA mRrj iwohZ ekWulwu 2004 ds fy, fuEu es?k ek=k] lkis{k vknzZrk] 'kq"d cYc rkieku rFkk vkSlr iou xfr ds ,u- ,- iSVuZ ls mRrj iwohZ ekWulwu 2002 ds ekeys esa bu ekSle foKkfud izkpyksa ds fy, ,u- ,- iSVuZ esa lw[ks tSls fLFkfr;ksa ds izs{k.kksa dh iqf"V gqbZA It is often helpful to express the meteorological data in terms of normalized anomalies as they make it easier to discern normal versus unusual values. Also it removes influence of location and spread from data and facilitates the comparison of observations at two different locations. Thus, Normalized Anomaly (NA) patterns i.e., spatial distribution of anomalies at specified time make a powerful tool in hand of forecasters to analyze extreme events. The present study explores the utilization of NA patterns for the purpose of analyzing extreme events by focusing on the inter-seasonal peculiar behavior of Northeast monsoon 2002. A detailed discussion is given and reasons are explored for droughts like situations during Northeast monsoon 2002. It was also noticed that the persistence of 200 hPa upper level ridge or positive geopotential height anomaly, negative mean sea level pressure anomaly over Siberian High during November, strength of 200 hPa wind anomaly can be one of the reasons for drought-like situation observed in the inter-seasonal behavior pattern of Northeast monsoon 2002. NA patterns of low cloud amount, dry bulb temperature and relative humidity captured drought-like situations during Northeast monsoon 2002 while NA of average wind speed captured the scenario of dissipating cyclones in the Bay of Bengal itself and not reaching to Peninsular India. The NA patterns of low cloud amount, relative humidity, dry bulb temperature and average wind speed for Northeast Monsoon 2004 confirm the observations of drought like situations seen in NA patterns for these meteorological parameters in case of Northeast monsoon 2002.

Emergent Constraints on CMIP6 Climate Warming Projections: Contrasting Cloud- and Surface Temperature-Based Constraints

The latest Sixth Coupled Model Intercomparison Project (CMIP6) multi-model ensemble shows a broader range of projected warming than the previous-generation CMIP5 ensemble. We show that the projected warming is well-correlated with tropical and subtropical low-level cloud properties. These physically-meaningful relations enable us to use observed cloud properties to constrain future climate warming. We develop multivariate-linear-regression models with metrics selected from a set of potential constraints based on a step-wise selection approach. The resulting linear regression model using two low-cloud metrics shows better cross-validated results than regression models which use single metrics as constraints. Application of a regression model using the low-cloud metrics to climate projections results in similar estimates of the mean, but substantially-narrower ranges, of projected 21st century warming when compared with unconstrained simulations. The resulting projected global-mean warming in 2081-2100 relative to 1995-2014 is 2.84-5.12 K (5-95% range) for Shared Socioeconomic Pathway (SSP) 5-8.5, compared with a range of 2.34-5.81 K for unconstrained projections, and 0.60-1.70 K for SSP1-2.6, compared to an unconstrained range of 0.38-2.04 K. We provide evidence for a higher lower-bound of the projected warming range than that obtained from constrained projections based on the past global-mean temperature trend. Consideration of the impact of the sea surface temperature pattern effect on the recent observed warming trend, which is not well-captured in the CMIP6 ensemble, indicates that the relatively-low projected warming resulting from the global-mean temperature trend constraint may not be reliable and provides further justification for the use of climatologically-based cloud metrics to constrain projections.

Low Cloud Detection in Multilayer Scenes using Satellite Imagery with Machine Learning Methods

The detection of multilayer clouds in the atmosphere can be particularly challenging from passive visible and infrared imaging radiometers since cloud boundary information is limited primarily to the topmost cloud layer. Yet detection of low clouds in the atmosphere is important for a number of applications, including aviation nowcasting and general weather forecasting. In this work, we develop pixel-based machine learning-based methods of detecting low clouds, with a focus on improving detection in multilayer cloud situations and specific attention given to improving the Cloud Cover Layers (CCL) product, which assigns cloudiness in a scene into vertical bins. The Random Forest (RF) and Neural Network (NN) implementations use inputs from a variety of sources, including GOES Advanced Baseline Imager (ABI) visible radiances, infrared brightness temperatures, auxiliary information about the underlying surface, and relative humidity (which holds some utility as a cloud proxy). Training and independent validation enlists near-global, actively-sensed cloud boundaries from the radar and lidar systems onboard the CloudSat and CALIPSO satellites. We find that the RF and NN models have similar performances. The probability of detection (PoD) of low cloud increases from 0.685 to 0.815 when using the RF technique instead of the CCL methodology, while the false alarm ratio decreases. The improved PoD of low cloud is particularly notable for scenes that appear to be cirrus from an ABI perspective, increasing from 0.183 to 0.686. Various extensions of the model are discussed, including a nighttime-only algorithm and expansion to other satellite sensors.

Variability and trends in low cloud cover over India during 1961-2010

Annual and seasonal variability and trends in low cloud cover over India were analyzed for the period 1961-2010. Taking all period into account, there is a general decrease in mean low cloud cover over most regions of India, but an increase in the Indo-Gangetic plains and northeast India. Long term mean low cloud cover over India has inter-annual variations with highest cloud cover (39.4%) in monsoon and lowest cloud cover (10.5%) in winter season. The annual mean low cloud cover shows significant decreasing trend of -0.45% per decade, mainly contributed by monsoon where declining rate is -1.22% per decade. Out of the total numbers of stations showing decreasing trends, 65%, 47%, 53%, 71% and 37% of the stations show significant decrease in low cloud cover for annual, winter, summer, monsoon and post monsoon respectively, with large trend magnitudes occurring in central India. Spatially, the seasonal patterns of trends in low cloud cover confirm the annual patterns in most cases. Data analyses show that low cloud cover is having a strong negative correlation with maximum temperature and diurnal temperature range and a strong positive correlation with numbers of rainy days during the period of study.

A meteorological overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) campaign over the southeastern Atlantic during 2016–2018: Part 1 – Climatology

In 2016–2018, the ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project undertook 3-month-long deployments to the southeastern (SE) Atlantic Ocean using research aircraft to better understand the impact of biomass burning (BB) aerosol transport to the SE Atlantic Ocean on climate. In this (part 1 of the meteorological overview) paper, the climatological features at monthly timescales are investigated. The southern African easterly jet (AEJ-S), defined as the zonal easterlies over 600–700 hPa exceeding 6 m s−1 around 5–15∘ S, is a characteristic feature of the mid-level circulation over southern Africa that was also during the deployment months of August 2017, September 2016, and October 2018. Climatologically, the AEJ-S develops at lower altitudes (∼ 3 km; 700 hPa) between 5–10∘ S in August, while it develops at around 4 km (∼ 600 hPa) and further south (5–15∘ S) in September and October, largely driven by the strong sensible heating over the African plateau. Notable meteorological anomalous characteristics during the 3 deployment months, compared to climatology (2000–2018), include the following: (1) during August 2017, the AEJ-S was weaker than the climatological mean, with an additional anomalous upper-level jet aloft (∼ 6 km) around 10∘ S. August 2017 was also drier over the SE Atlantic at 600–700 hPa than climatology, with a stronger Benguela low-level jet (LLJ) at 925–950 hPa along the Namibian coast of the SE Atlantic. Consistent with this, the southern Atlantic anticyclone was also stronger and closer to the coast than the August climatological mean. (2) During September 2016, the AEJ-S intensity was similar to the climatological mean, although the heat low and vertical motion over the land was slightly stronger compared to the September climatology. The LLJ and the large-scale southern Atlantic anticyclone were stronger than the climatological mean. (3) During October 2018, the AEJ-S was slightly weaker compared to the climatological mean, as was the LLJ and the southern Atlantic anticyclone. October 2018 was wetter over the Benguela coastal region at 600 hPa than the climatological mean. During all the deployment months, the sea surface temperatures (SST) over the SE Atlantic were warmer than the climatological means, but the monthly mean low cloud fraction was only noticeably reduced in August 2017. A weak August 2017 AEJ-S can explain low offshore black carbon (BC) mixing ratios within the European Centre for Medium-Range Weather Forecasts (ECMWF) Copernicus Atmosphere Monitoring Service (CAMS) reanalysis, although the BC peak altitude, at 2–3 km, is below that of the AEJ-S. The upper-level wave disturbance and the associated anomalous circulation also explain the weakening of AEJ-S through the reduction of the strength of the heat low over the land during August 2017.

Aerosol-boundary-layer-monsoon interactions amplify semi-direct effect of biomass smoke on low cloud formation in Southeast Asia

Low clouds play a key role in the Earth-atmosphere energy balance and influence agricultural production and solar-power generation. Smoke aloft has been found to enhance marine stratocumulus through aerosol-cloud interactions, but its role in regions with strong human activities and complex monsoon circulation remains unclear. Here we show that biomass burning aerosols aloft strongly increase the low cloud coverage over both land and ocean in subtropical southeastern Asia. The degree of this enhancement and its spatial extent are comparable to that in the Southeast Atlantic, even though the total biomass burning emissions in Southeast Asia are only one-fifth of those in Southern Africa. We find that a synergetic effect of aerosol-cloud-boundary layer interaction with the monsoon is the main reason for the strong semi-direct effect and enhanced low cloud formation in southeastern Asia.

West African monsoon precipitation impacted by the South Eastern Atlantic biomass burning aerosol outflow

The West African Monsoon (WAM) is a complex system depending on global climate influences and multiple regional environmental factors. Central and Southern African biomass-burning (SABB) aerosols have been shown to perturb WAM during episodic northward inter-hemispheric transport events, but a possible dynamical connection between the core of the SABB aerosol outflow and the WAM system remains unexplored. Through regional climate modeling experiments, we show that SABB aerosols can indeed impact WAM dynamics via two competitive regional scale and inter-hemispheric dynamical feedbacks originating from (i) enhanced diabatic heating occurring in the Southeastern Atlantic low-cloud deck region, and (ii) aerosol and cloud-induced sea surface temperature cooling. These mechanisms, related to aerosol direct, semi-direct, and indirect effects, are shown to have different seasonal timings, resulting in a reduction of June to September WAM precipitation, while possibly enhancing late-season rainfall in WAM coastal areas.